#This code produces Table 2 and Figures 6 and 7 in appendix C

if(!dir.exists("figs")){dir.create("figs")}
if(!dir.exists("tabs")){dir.create("tabs")}

dir.create("tabs/k20")
dir.create("figs/k20")

library(tidyverse)
library(arabicStemR)
library(tidytext)
library(lubridate)
library(ggplot2)
library(quanteda)
library(stm)
library(stats)
library(ggthemes)
library(ggpubr)

rm(list = ls())
gc()

# 20 topics -------------------------------------------------------------
load("k20.RData")

meta = meta %>% mutate(document = article) %>% select(- article)

beta = tidy(topic_model)
gamma = tidy(topic_model, matrix = "gamma")

gamma = left_join(gamma, meta, by = "document")
gc()


terms_final = tibble(topic = character(),
                         terms = character())

terms_final = terms_final %>% 
  add_row(topic = "Media and education", terms = c("media, university, culture, public, education")) %>% 
  add_row(topic = "Assad and nation", terms = c("nation, president, people, party, leadership, Assad")) %>% 
  add_row(topic = "US and Iran", terms = c("America, united, states, Iran, world")) %>% 
  add_row(topic = "temperature and weather", terms = c("region, degree, temperature, south, north, governorate")) %>% 
  add_row(topic = "Israel and Palestine", terms = c("Israel, Palestine, occupation, Jerusalem, land")) %>% 
  add_row(topic = "terrorism", terms = c("terrorism, terror, army, armed, group, destroy")) %>% 
  add_row(topic = "Gulf and religion", terms = c("Saud, Islam, Tunisia, Qatar, religion")) %>% 
  add_row(topic = "Russia and intl community", terms = c("Russia, foreign, united, nations, security, council")) %>% 
  add_row(topic = "economy and development", terms = c("council, public, economy, project, sector, investment")) %>% 
  add_row(topic = "conspiracies and plots", terms = c("Syria, people, terrorism, Zionist, resistance, conspiracy")) %>% 
  add_row(topic = "diplomacy", terms = c("president, relations, mister, visit, meeting, foreign")) %>% 
  add_row(topic = "legislation", terms = c("article, law, declaration, council, number, legislation")) %>% 
  add_row(topic = "accidents and deaths", terms = c("car, road, said, city, transport")) %>% 
  add_row(topic = "speeches", terms = c("said, president, states, discussion, politics")) %>% 
  add_row(topic = "victims", terms = c("citizen, Aleppo, terror, aid, humanitarian, nation")) %>% 
  add_row(topic = "turkey", terms = c("Turkey, Erdogan, government, party, people, justice")) %>% 
  add_row(topic = "national unity", terms = c("nation, Syrian, martyrs, son, state, people")) %>% 
  add_row(topic = "ISIS", terms = c("terrorism, organization, ISIS, group, armed")) %>% 
  add_row(topic = "Iraq", terms = c("Iraq, Baghdad, America, kill, injure")) %>% 
  add_row(topic = "Lebanon", terms = c("Lebanon, resistance, Israel, army"))


topic_avg = gamma %>% 
  group_by(topic) %>% 
  summarise(avg = mean(gamma)) %>% 
  arrange(-avg)

terms_final = terms_final %>% mutate(topic2 = 1:nrow(terms_final)) %>% left_join(topic_avg, by = c("topic2" = "topic")) %>% select(-topic2)

names(terms_final) = c("Topic Label", "High Probability Terms", "Expected Proportion")

terms_final %>% arrange(desc(`Expected Proportion`)) %>% 
  xtable::xtable(caption = "Topic labels, highest probability terms, and expected proportion for n = 20 topics",
                 label = "tab:topics") %>% 
  xtable::print.xtable(file = "tabs/k20/topics.tex", include.rownames = F)


topics = tibble(topic = 1:20, `Topic Label` = terms_final$`Topic Label`)

gamma = gamma %>% ungroup %>% left_join(topics)

gamma = gamma %>% select(-topic) %>% rename(topic = `Topic Label`)

gamma = gamma %>% 
  select(document, topic, date, gamma) %>% 
  filter(nchar(topic) > 2) %>% 
  group_by(topic, date) %>% 
  summarise(avg = mean(gamma))


gamma2 = gamma %>% 
  spread(topic, avg)

gamma3 = gamma2 %>% 
  mutate(week = floor_date(date, "week")) %>% 
  select(date, week, everything()) %>% 
  group_by(week) %>% 
  mutate_if(.predicate = is.numeric, .funs = mean)

names(gamma3)[3:ncol(gamma3)] = paste0(names(gamma3)[3:ncol(gamma3)], "_fit")

gamma3 = gamma3 %>% left_join(gamma2)

plot_fun = function(y1){
  y2 = paste0(y1, "_fit")
  title = tools::toTitleCase(y1)
  
  df = gamma3[, c("date", y1, y2)] %>% as.data.frame()
  
  p = ggplot() + 
    geom_line(aes(x = df$date, y = df[,y1], colour = y1)) + 
    geom_line(aes(x = df$date, y = df[,y2], colour = y2)) + 
    geom_vline(xintercept = dmy("15-03-2011"), linetype = 2) + 
    scale_colour_manual(values = c("darkgrey", "black")) + 
    labs(title = title, y = expression(paste("Average  ", gamma)), x = "Year") + theme_few() + 
    theme(legend.position = "none", title = element_text(size = 20),
          axis.text = element_text(size = 14)) + 
    coord_cartesian(ylim = c(0, 0.25)) + 
    scale_x_date(date_breaks = "2 year", date_labels = "%y") + 
    scale_y_continuous(breaks = c(0, 0.10, 0.20))
  p %>% ggsave(filename = paste0("figs/k20/", y1, ".pdf"), width = 6, height = 5)
  return(plot)
}



plot_names = names(gamma3)[!str_detect(names(gamma3), "fit|date|week")]

for(i in 1:length(plot_names)){
  plot_fun(y1 = plot_names[i])
}
